Most scheduling applications have been demonstrated as NP-complete problems. A variety of schemes are introduced in solving those scheduling applications, such as linear programming, neural networks, and fuzzy logic. In this paper, a new approach of first analogising a scheduling problem to a clustering problem and then using a fuzzy Hopfield neural network clustering technique to solve the scheduling problem is proposed. This fuzzy Hopfield neural network algorithm integrates fuzzy c-means clustering strategies into a Hopfield neural network. This investigation utilises this new approach to demonstrate the feasibility of resolving a multiprocessor scheduling problem with no process migration and constrained times (execution time and deadline). Each process is regarded as a data sample, and every processor is taken as a cluster. Simulation results illustrate that imposing the fuzzy Hopfield neural network onto the proposed energy function provides an appropriate approach to solving this class of scheduling problem.
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Chen, RM., Huang, YM. Multiprocessor Task Assignment with Fuzzy Hopfield Neural Network Clustering Technique . Neural Computing & Applications 10, 12–21 (2001). https://doi.org/10.1007/s005210170013
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DOI: https://doi.org/10.1007/s005210170013